Setup

## **** __Utilized Cores__ **** = 2$subsetGenes
## [1] "protein_coding"
## 
## $subsetCells
## [1] 500
## 
## $resolution
## [1] 0.6
## 
## $resultsPath
## [1] "./Results"
## 
## $nCores
## [1] 2
## 
## $perplexity
## [1] 30
## [1] "Written using: Seurat version* 2.3.4 2018-07-17"
## [1] "Seurat 2.3.4"
## [1] "monocle3 0.1.2"
## [1] "garnett 0.2.3"

Preprocessing

Seurat/2.3.4 to Monocle3

Also subset to only protein-coding genes.

## Subsetting genes: protein_coding
## Object representation is consistent with the most current Seurat version.
## [1] "Processing..."
## [1] "+ Expression data"
## [1] "+ Phenotype data"
## [1] "+ Feature Data"
## [1] "Expression Data dims:"
## [1] 24914 19144
## [1] "Metadata dims:"
## [1] 19144    17
## [1] "Feature Data dims:"
## [1] 24914     1
## [1] "+ Converting to monocle (Version 3)"

Clustering

  • Unsupervised clustering of cells is a common step in many single-cell expression workflows. In an experiment containing a mixture of cell types, each cluster might correspond to a different cell type. - This function takes a cell_data_set as input, clusters the cells using Louvain community detection, and returns a cell_data_set with internally stored cluster assignments.
  • In addition to clusters this function calculates partitions, which represent superclusters of the Louvain communities that are found using a kNN pruning method. Cluster assignments can be accessed using the clusters function and partition assignments can be accessed using the partitions function.

  • Using only the topN variable genes to cluster

Louvain Clustering

Pseudotime

## 
  |                                                                       
  |                                                                 |   0%
  |                                                                       
  |=================================================================| 100%
## Warning in louvain_clustering(data, pd[row.names(data), ], k = k, weight = weight, : RANN counts the point itself, k must be smaller than
## the total number of points - 1 (all other points) - 1 (itself)!

## Warning: Transformation introduced infinite values in continuous y-axis
## Warning: Transformation introduced infinite values in continuous y-axis

UMAP Plots

Disease status (dx), mutation status (mut), and individual ID (ID) show good mixture across clusters.

dx

mut

ID

Cell-type Identication

Garnett

Use a pre-trained classifier from Pline et al.

## Loading required package: AnnotationDbi
## 
## Attaching package: 'AnnotationDbi'
## The following object is masked from 'package:plotly':
## 
##     select
## The following object is masked from 'package:dplyr':
## 
##     select
## 
##  [1] "ACCNUM"       "ALIAS"        "ENSEMBL"      "ENSEMBLPROT" 
##  [5] "ENSEMBLTRANS" "ENTREZID"     "ENZYME"       "EVIDENCE"    
##  [9] "EVIDENCEALL"  "GENENAME"     "GO"           "GOALL"       
## [13] "IPI"          "MAP"          "OMIM"         "ONTOLOGY"    
## [17] "ONTOLOGYALL"  "PATH"         "PFAM"         "PMID"        
## [21] "PROSITE"      "REFSEQ"       "SYMBOL"       "UCSCKG"      
## [25] "UNIGENE"      "UNIPROT"
## Warning in doTryCatch(return(expr), name, parentenv, handler): The
## following genes used in the classifier are not present in the input CDS.
## Interpret with caution. ENSG00000174059The following genes used in the
## classifier are not present in the input CDS. Interpret with caution.
## ENSG00000007062The following genes used in the classifier are not present
## in the input CDS. Interpret with caution. ENSG00000157404The following
## genes used in the classifier are not present in the input CDS. Interpret
## with caution. ENSG00000185291

Save Checkpoint

Save R object and run memory-intensive DGE analyses on computing cluster.